{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experiment: \n",
    "\n",
    "Evaluate pruning by magnitude weighted by coactivations (more thorough evaluation), compare it to baseline (SET).\n",
    "\n",
    "#### Motivation.\n",
    "\n",
    "Check if results are consistently above baseline.\n",
    "\n",
    "#### Conclusion\n",
    "\n",
    "- No significant difference between both models\n",
    "- No support for early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import tabulate\n",
    "import pprint\n",
    "import click\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ray.tune.commands import *\n",
    "from nupic.research.frameworks.dynamic_sparse.common.browser import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gsc-SET\n",
      "gsc-WeightedMag\n"
     ]
    }
   ],
   "source": [
    "base = os.path.join('gsc-dsnn-2019-10-11-G-reproduce')\n",
    "exps = [\n",
    "    os.path.join(base, exp) for exp in [\n",
    "#         'gsc-Static', \n",
    "        'gsc-SET',\n",
    "        'gsc-WeightedMag',\n",
    "    ]\n",
    "]\n",
    "paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
    "df = load_many(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "Name: hebbian_prune_perc, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    None-None-0.3-0.3\n",
       "1    None-None-0.3-0.3\n",
       "2    None-None-0.3-0.3\n",
       "3    None-None-0.3-0.3\n",
       "4    None-None-0.3-0.3\n",
       "Name: weight_prune_perc, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# replace hebbian prine\n",
    "df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
    "df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
    "display(df.head(5)['hebbian_prune_perc'])\n",
    "display(df.head(5)['weight_prune_perc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment Name</th>\n",
       "      <th>train_acc_max</th>\n",
       "      <th>train_acc_max_epoch</th>\n",
       "      <th>train_acc_min</th>\n",
       "      <th>train_acc_min_epoch</th>\n",
       "      <th>train_acc_median</th>\n",
       "      <th>train_acc_last</th>\n",
       "      <th>val_acc_max</th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th>val_acc_min</th>\n",
       "      <th>...</th>\n",
       "      <th>model</th>\n",
       "      <th>momentum</th>\n",
       "      <th>net_params</th>\n",
       "      <th>network</th>\n",
       "      <th>on_perc</th>\n",
       "      <th>optim_alg</th>\n",
       "      <th>prune_methods</th>\n",
       "      <th>test_noise</th>\n",
       "      <th>weight_decay</th>\n",
       "      <th>weight_prune_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Experiment Name, train_acc_max, train_acc_max_epoch, train_acc_min, train_acc_min_epoch, train_acc_median, train_acc_last, val_acc_max, val_acc_max_epoch, val_acc_min, val_acc_min_epoch, val_acc_median, val_acc_last, val_acc_all, epochs, experiment_file_name, experiment_base_path, trial_time, mean_epoch_time, scatter_plot_dicts, batch_size_test, batch_size_train, data_dir, dataset_name, debug_sparse, debug_weights, device, hebbian_grow, hebbian_prune_perc, learning_rate, lr_gamma, lr_milestones, lr_scheduler, model, momentum, net_params, network, on_perc, optim_alg, prune_methods, test_noise, weight_decay, weight_prune_perc]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 43 columns]"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[200:205]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
       "       'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
       "       'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
       "       'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'val_acc_all',\n",
       "       'epochs', 'experiment_file_name', 'experiment_base_path', 'trial_time',\n",
       "       'mean_epoch_time', 'scatter_plot_dicts', 'batch_size_test',\n",
       "       'batch_size_train', 'data_dir', 'dataset_name', 'debug_sparse',\n",
       "       'debug_weights', 'device', 'hebbian_grow', 'hebbian_prune_perc',\n",
       "       'learning_rate', 'lr_gamma', 'lr_milestones', 'lr_scheduler', 'model',\n",
       "       'momentum', 'net_params', 'network', 'on_perc', 'optim_alg',\n",
       "       'prune_methods', 'test_noise', 'weight_decay', 'weight_prune_perc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 43)"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Experiment Name              1_model=SET,on_perc=[0.04, 0.04, 0.04, 0.04]\n",
       "train_acc_max                                                    0.852602\n",
       "train_acc_max_epoch                                                    98\n",
       "train_acc_min                                                    0.291378\n",
       "train_acc_min_epoch                                                     0\n",
       "train_acc_median                                                 0.825774\n",
       "train_acc_last                                                   0.850991\n",
       "val_acc_max                                                      0.914577\n",
       "val_acc_max_epoch                                                      94\n",
       "val_acc_min                                                      0.459639\n",
       "val_acc_min_epoch                                                       0\n",
       "val_acc_median                                                   0.882053\n",
       "val_acc_last                                                     0.892633\n",
       "val_acc_all             0     0.459639\n",
       "1     0.687696\n",
       "2     0.800157\n",
       "3...\n",
       "epochs                                                                100\n",
       "experiment_file_name    /Users/mcaporale/nta/results/gsc-dsnn-2019-10-...\n",
       "experiment_base_path                                              gsc-SET\n",
       "trial_time                                                        15.5704\n",
       "mean_epoch_time                                                  0.155704\n",
       "scatter_plot_dicts                                                     {}\n",
       "batch_size_test                                                      1000\n",
       "batch_size_train                                                       10\n",
       "data_dir                                               ~/nta/datasets/gsc\n",
       "dataset_name                                              PreprocessedGSC\n",
       "debug_sparse                                                         True\n",
       "debug_weights                                                        True\n",
       "device                                                               cuda\n",
       "hebbian_grow                                                        False\n",
       "hebbian_prune_perc                                                      0\n",
       "learning_rate                                                        0.01\n",
       "lr_gamma                                                              0.9\n",
       "lr_milestones                                                          60\n",
       "lr_scheduler                                                  MultiStepLR\n",
       "model                                                                 SET\n",
       "momentum                                                                0\n",
       "net_params              {'boost_strength': 1.5, 'boost_strength_factor...\n",
       "network                                                   gsc_sparse_dsnn\n",
       "on_perc                                                              0.04\n",
       "optim_alg                                                             SGD\n",
       "prune_methods                                         None-None-None-None\n",
       "test_noise                                                          False\n",
       "weight_decay                                                         0.01\n",
       "weight_prune_perc                                       None-None-0.3-0.3\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "DSNNWeightedMag    10\n",
       "SET                10\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('model')['model'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment Details"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# experiment configurations\n",
    "base_exp_config = dict(\n",
    "    device=(\"cuda\" if torch.cuda.device_count() > 0 else \"cpu\"),\n",
    "    # dataset related\n",
    "    dataset_name=\"PreprocessedGSC\",\n",
    "    data_dir=\"~/nta/datasets/gsc\",\n",
    "    batch_size_train=16,\n",
    "    batch_size_test=(1000),\n",
    "    # network related\n",
    "    network=\"GSCHeb\",\n",
    "    # ----- Optimizer Related ----\n",
    "    optim_alg=\"SGD\",\n",
    "    momentum=0.0,\n",
    "    learning_rate=0.01,\n",
    "    weight_decay=1e-2,\n",
    "    # ----- LR Scheduler Related ----\n",
    "    lr_scheduler=\"StepLR\",\n",
    "    lr_step_size=1,\n",
    "    lr_gamma=0.9,\n",
    "    # additional validation\n",
    "    test_noise=False,\n",
    "    # debugging\n",
    "    debug_weights=True,\n",
    "    debug_sparse=True,\n",
    ")\n",
    "\n",
    "# ray configurations\n",
    "# experiment_name = \"gsc-trials-2019-10-07\"\n",
    "experiment_name = \"gsc-dsnn-2019-10-11\"\n",
    "tune_config = dict(\n",
    "    name=experiment_name,\n",
    "    num_samples=5,\n",
    "    local_dir=os.path.expanduser(os.path.join(\"~/nta/results\", experiment_name)),\n",
    "    checkpoint_freq=0,\n",
    "    checkpoint_at_end=False,\n",
    "    stop={\"training_iteration\": 30},\n",
    "    resources_per_trial={\n",
    "        \"cpu\": os.cpu_count() / torch.cuda.device_count(),\n",
    "        \"gpu\": 1,\n",
    "    },\n",
    "    loggers=DEFAULT_LOGGERS,\n",
    "    verbose=1,\n",
    "    config=base_exp_config,\n",
    ")\n",
    "\n",
    "# define experiments\n",
    "on_perc_levels = np.arange(0, 0.101, 0.005)\n",
    "experiments = {\n",
    "\n",
    "    \"gsc-Static\": dict(\n",
    "        model=ray.tune.grid_search([\"SparseModel\"]),\n",
    "        network=\"gsc_sparse_dsnn\",\n",
    "        # sparse related\n",
    "        on_perc=ray.tune.grid_search([\n",
    "            [perc] * 4 for perc in\n",
    "            on_perc_levels\n",
    "        ]),\n",
    "    ),\n",
    "\n",
    "    \"gsc-SET\": dict(\n",
    "        model=ray.tune.grid_search([\"SET\"]),\n",
    "        network=\"gsc_sparse_dsnn\",\n",
    "        # network related\n",
    "        prune_methods=[None, None, None, None],\n",
    "        # sparse related\n",
    "        on_perc=ray.tune.grid_search([\n",
    "            [perc] * 4 for perc in\n",
    "            on_perc_levels\n",
    "        ]),\n",
    "        hebbian_prune_perc=None,\n",
    "        hebbian_grow=False,\n",
    "        weight_prune_perc=[None, None, 0.3, 0.3],\n",
    "    ),\n",
    "\n",
    "    \"gsc-WeightedMag\": dict(\n",
    "        model=ray.tune.grid_search([\"DSNNWeightedMag\"]),\n",
    "        network=\"gsc_sparse_dsnn\",\n",
    "        # network related\n",
    "        prune_methods=[None, None, \"dynamic-linear\", \"dynamic-linear\"],\n",
    "        # sparse related\n",
    "        on_perc=ray.tune.grid_search([\n",
    "            [perc] * 4 for perc in\n",
    "            on_perc_levels\n",
    "        ]),\n",
    "        hebbian_prune_perc=None,\n",
    "        hebbian_grow=False,\n",
    "        weight_prune_perc=[0.3, 0.3],\n",
    "    ),\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Did any  trials failed?\n",
    "df[df[\"epochs\"]<30][\"epochs\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 43)"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing failed or incomplete trials\n",
    "df_origin = df.copy()\n",
    "df = df_origin[df_origin[\"epochs\"]>=30]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: epochs, dtype: int64)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which ones failed?\n",
    "# failed, or still ongoing?\n",
    "df_origin['failed'] = df_origin[\"epochs\"]<30\n",
    "df_origin[df_origin['failed']]['epochs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def mean_and_std(s):\n",
    "    return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
    "\n",
    "def round_mean(s):\n",
    "    return \"{:.0f}\".format(round(s.mean()))\n",
    "\n",
    "stats = ['min', 'max', 'mean', 'std']\n",
    "\n",
    "def agg(columns, filter=None, round=3):\n",
    "    if filter is None:\n",
    "        return (df.groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "    else:\n",
    "        return (df[filter].groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Does improved weight pruning outperforms regular SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>83</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.037</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>88</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.035</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                val_acc_max_epoch val_acc_max                      model\n",
       "                       round_mean         min    max   mean    std count\n",
       "model                                                                   \n",
       "DSNNWeightedMag                83       0.841  0.924  0.883  0.037    10\n",
       "SET                            88       0.841  0.922  0.886  0.035    10"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_perc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>88</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.008</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>83</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        val_acc_max_epoch val_acc_max                      model\n",
       "               round_mean         min    max   mean    std count\n",
       "on_perc                                                         \n",
       "0.02                   88       0.841  0.867  0.851  0.008    10\n",
       "0.04                   83       0.908  0.924  0.918  0.004    10"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['on_perc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_name(row):\n",
    "    \n",
    "    if row['model'] == 'DSNNWeightedMag':\n",
    "        return 'DSNN'\n",
    "\n",
    "    elif row['model'] == 'SET':\n",
    "        return 'SET'\n",
    "\n",
    "    elif row['model'] == 'SparseModel':\n",
    "        return 'Static'\n",
    "    \n",
    "    assert False, \"This should cover all cases. Got {} h - {} w - {}\".format(row['model'], row['hebbian_prune_perc'], row['weight_prune_perc'])\n",
    "\n",
    "df['model2'] = df.apply(model_name, axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_perc</th>\n",
       "      <th>model2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.02</th>\n",
       "      <th>DSNN</th>\n",
       "      <td>83</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>93</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.009</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.04</th>\n",
       "      <th>DSNN</th>\n",
       "      <td>83</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>84</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.003</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               val_acc_max_epoch val_acc_max                      model\n",
       "                      round_mean         min    max   mean    std count\n",
       "on_perc model2                                                         \n",
       "0.02    DSNN                  83       0.841  0.854  0.849  0.006     5\n",
       "        SET                   93       0.841  0.867  0.853  0.009     5\n",
       "0.04    DSNN                  83       0.908  0.924  0.918  0.006     5\n",
       "        SET                   84       0.915  0.922  0.918  0.003     5"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fltr = (df['model2'] != 'Sparse') & (df['lr_scheduler'] == \"MultiStepLR\")\n",
    "agg(['on_perc', 'model2'], filter=fltr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "# translate model names\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "# d = {\n",
    "#     'DSNNWeightedMag': 'DSNN',\n",
    "#     'DSNNMixedHeb': 'SET',\n",
    "#     'SparseModel': 'Static',        \n",
    "# }\n",
    "# df_plot = df.copy()\n",
    "# df_plot['model'] = df_plot['model'].apply(lambda x, i: model_name(x, i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fffdGjw2S6qmVgw8+OL/+9a8zYcKERkFukgwbNiwnnHBCli1blp///Odttj9Qr1rqZH0/+9nPkiTnnHNOs3fltmvXLk8//XQeffTRXHDBBY32a8CAAbnkkkuSJHfeeedGjgzWKkutXH/99Zk2bVoefPDBHHnkkXnwwQczdOjQ1NbWbtb81hTaWrXUSnPee++9/NVf/VVeffXV9O7du9F7Eq0rtLVqqZVNPU+3rtDWqqVW1nfHHXektrY2p5xySot3FP7bv/1bJkyYkKuuuqohyE2Snj175mtf+1oS6wqtt6Vq5c0338x3v/vd9O/fP0OHDm3z+a0rrSPM5SPVpUuXJMnKlStbHFPft6ErTOrnWbFixQbnqB+3vqeffjpDhw7NokWLctpppzV5h05r56/fx00dDxtTLbWyvmnTpuWLX/xi5s2bl6OPPjrXX399k180unXrlr322qvZzw8aNChJGt6jCBtTTbXSvXv37L777s1+vv5nf8aMGZu0P9YVWqOa6qTe8uXL89RTTyXZ8Huqtttuu+y7777N9tXfNWVNobXKUiu77LJLOnXqlAMPPDA//OEPc8ABB+QPf/hDw+P42/pcZdWqVRs9JlhXtdTK+t5444188YtfzIsvvpjevXvnlltuabJ/1hXaUjXVyqacp1tXaGvVVCvrqm8/5ZRTNrhPlUql2ZDq2GOPzVZbbZVZs2Z97O84pHW2RK3Uvy4sScaMGdNi4Lo58ztf2TTCXD5SXbt2TZcuXbJw4cJmr2qqra3NwoUL06lTp2y//fYtzlP/nPSW3nGwoeesjx8/PhdddFGWLVuW888/P9dee22TsGlT5/+f7A80p1pqZV2/+tWvct555+Xdd9/NSSedlB/+8IfN3km1IfX70dLiDeurxlppzvo/+9YV2lI11skzzzyTFStW5NBDD23xIoiN2XnnnZNYU2i9MtTK+rbeeuv83//7f5P89zvX2vpcZWPvzIL1VUutrOuFF17ImWeemdmzZ+eII47Iz372s+y4444bnXdd1hU2VTXWSnM29VzFusKmqsZamTFjRubNm5cePXrksMMO2+h8LW3jE5/4RBJrC62zJWrlsccey9SpU7PTTjvlX//1X/PVr3614Z/f/va3Sdbegf7Vr341EydO3OT5WzPeurKWMJePVLt27bLffvtlzZo1mT17dpP+WbNmpa6uruGdNS3Zf//9kyQzZ85s0lcURV5//fVstdVWTa6o/cEPfpBrrrkmRVFk1KhRufLKK5u9mmRD8yfJa6+9liQ54IADkqRhfzc2fmPHBfWqpVbq3X///bnkkkuyfPnynH/++bnuuuvSsWPHJuOee+65XHbZZbntttuanWfu3LlJkl133XWDxwX1qqVWHn300Vx66aWZMGFCs9tf/2ffukJbqpY6WdfTTz+dJBk8eHCLY1566aVceeWVue6665rtr6+rXXbZZYPbgnpbqlZ++ctf5oorrsjzzz/f7Hz1v1PV/8FmU9eI/fbbL+3bt29oX9/rr7/eaDxsTLXUSr1nn302Q4cOzcKFC/O5z30ut9xyS7PvkLOu0NaqpVY29TzdukJbq5ZaWVf9+coJJ5zQ4kWq8+bNy1VXXZWrr7662f6lS5fmvffeS+fOnRtCXdiQLVEr9XeNz5kzJxMmTGj0z5tvvpkkmTp1aiZMmJCampokzlc+LMJcPnLHHHNMkuTxxx9v0lffduyxx7Zqjl/96ldN+n73u9/lvffey2GHHdboBO3222/PDTfckK233jrf//73W3wxfZL0798/nTt3zvPPP9/kMRdLly7N888/ny5dujRcebXvvvtm9913z0svvZS333670fi6uro88cQTadeuXcN+Q2tUQ63U78vVV1+durq6XHnllRv8I/2KFSvy8MMP52c/+1mzvww/+OCDSZKjjz56g9uEdVVDrSxYsCCPPPJIs++DLooiDz/8cJL//tm3rtDWqqFO1jVt2rQkSb9+/Vocs9VWW+XnP/95br/99ixZsqRJf/2aok7YFFuiVmbNmpUHH3ww48ePb3a+Z599NknSu3fvJJu+RtSftyxYsCC/+93vNvu4YF3VUCtJ8vvf/z4jR47MihUrMnTo0Hz/+99v9qLTxLrCh6MaamVTz9OtK3wYqqFW1tWa85WuXbvmoYceyr333ps33nijSf9DDz2UJBk4cGC22mqrDR0aNPioa+W0005LTU1Ns//8v//3/5KsffxyTU1NLrnkkiTOVz40BXzE5syZUxx88MFFv379iunTpze0T5s2rejXr1/Rp0+f4t13321of+ONN4qZM2cWixcvbmhbs2ZN8bnPfa6oVCrFPffc09C+YMGC4gtf+EJRqVSKX/3qVw3tr7zyStG7d++iUqkUjzzySKv28x/+4R+KSqVSXH755cXq1auLoiiK1atXF5dffnlRqVSKMWPGNBp/0003FZVKpTj//POLpUuXNrRfd911RaVSKUaOHNnKbwjWqoZaeeedd4rDDz+8qFQqxU033bTR8StXriwGDRpUVCqV4tprry1qa2sb+u67776iUqkUn/rUpxodA2xMNdTKggULin79+hWVSqW49dZbG9rr6uqKG264oahUKsXnP//5hvWmKKwrtK1qqJN6S5cuLQ488MCiV69exYoVKzY49swzzywqlUrxla98pdHYp59+ujjooIOKQw45pJgzZ06rtw1bolbeeuutok+fPkWlUikefvjhhvba2triX//1X4tKpVIMGjSo0c/4pq4R//7v/15UKpXi5JNPLhYsWNDQPn78+KJSqRSnnnrq5n5lfExVQ60sX768OP7444tKpVJ8/etfb9VxWVdoa9VQK5tznm5doa1VQ62s66ijjioqlUoxe/bsDR7X3//93xeVSqUYMmRI8f777ze0T58+vTjiiCOKnj17FlOnTm3ltwRbplZactVVVxWVSqW4//77m/Q5X2l77YqiKLZ0oMzHz5133pnRo0dn6623zoABA1IURSZNmpTa2tqMHTu20Yvjjz/++MybNy9jxozJaaed1tA+bdq0DB06NMuWLcshhxySHj165Le//W3ef//9nHHGGfmnf/qnhrF/93d/l0cffTTbb7/9Bq/gOOuss3L44YcnSRYtWpSzzjors2bNyp577plevXrlpZdeypw5c9KrV6/ccccd2XbbbRs+u2rVqgwbNixTpkxJ9+7d069fv8yaNSt/+MMf8slPfjL33HNPw/PfobXKXivf+973cvPNN2frrbfOZz/72RbvyD3xxBNz4oknJll7hdcFF1yQZcuW5c/+7M/Ss2fPzJkzJy+//HK6dOmSn/zkJ+nbt+//9KvjY6bstZKsfYTTpZdemtWrV2f//ffPPvvsk5qamsyePTvdu3fPHXfckb322qvhs9YV2lo11Emy9pFLJ510Unbbbbc89dRTGzym2bNn55xzzsn8+fPTo0eP9OnTJwsWLMjUqVPToUOH/PM//3PD+gOt9VHXSrL2rqerrroqa9asSe/evbPrrrvmlVdeybx589KtW7fccsst6dWrV8P4zVkj/vZv/zb/8R//kU984hM54ogj8qc//SnTpk3L9ttvn7vuuqvhcWvQWmWvlbvvvjv/+I//mCT57Gc/2+Iduf3798+ZZ56ZxLrCh6PstZJs3nm6dYW2Vg21kqy9m/2QQw5J+/btM3369HTo0KHFY3rvvfdy9tlnZ9asWdlhhx3St2/fLFu2LP/1X/+Vurq6XHXVVTnvvPPa6Bvk42JL1Epzvva1r+W+++5rMnfifOXDIMxli3nyySfz4x//OC+99FI6duyYAw44IBdddFEGDhzYaFxL/8NJ1j53/frrr8+kSZOyatWq/Pmf/3nOOuusnH766Y0eT/GpT32qxRdor2v9bSxatCg/+MEP8vjjj2fBggXZbbfdMnjw4HzpS1/Kdttt1+Tzy5cvz49+9KM88sgj+eMf/5ju3bvn6KOPzsiRI/3Bnc1W5lo544wz8vvf/36j40eOHNnwqI1k7R9JfvjDH+Y3v/lNFi1alB133DFHH310Lr744uy5554bnQ+aU+ZaqTdjxozcdNNNmTx5cpYsWZIePXrk+OOPz8UXX5xu3bo1+bx1hbZWDXUyefLknHPOOenbt2+Ljzxb1/z58/PDH/4wTz75ZObPn5/tttsu/fv3z8UXX5yePXtu9PPQnI+yVupNnTo1P/rRj/K73/0uS5cuTY8ePTJo0KCMGDGi2Xd0buoaUVtbmzvuuCP33Xdf3njjjey44445/PDD8+Uvf7nRxUSwKcpcK/UXFW3Mqaeemm9/+9sN/25d4cNQ5lqpt6nn6dYVPgzVUCtvvfVWBg0alO7du+fXv/71Ro9pyZIl+dGPfpRf/vKXmTdvXrp06ZJDDjkkw4cPz5FHHrmJ3xCstSVqZX0bCnMT5yttTZgLAAAAAMD/197dhWhZtW0APh1GM7U0y1ExNX9SYkRUlBnDqBQzQsFqQ7QkSdJyoyL7M8kQs4w0wUJIEYRSyYkxKjIFK82yJCQUR4lqxhLKCcwy/3/m2xDnff1mfLNPy+fzPY6dh2eta933tbZP7rUAgAJUdLEbAAAAAAAAAKAhYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAA8F9h+/btmTp1am666ab06dMn5eXlefDBB/P555+fUbd79+707t07I0eOzPHjx7N48eKMHDkyffv2TVlZWaZMmZJt27ZdkJ4qKyvTu3fvzJgxI3v27MnUqVNTXl6e/v3756677srKlStz8uTJRtcePXo0S5cuzd13353+/funX79+ufPOO7NkyZIcOXKkQf348ePTu3fvbNmyJTNnzsyAAQMyYMCATJgw4Yx3fPLJJ5k8eXKGDBmSPn36ZOjQoXnuuedSW1t7QfYMAADAuWtSV1dXd7GbAAAAgL/TsmXLMnv27Jw4cSKtW7dOly5d8vPPP+eXX35JkkycODFPPvlkklNh7rBhw9KtW7d06tQpGzduzDXXXJOSkpJ8++23OXr0aJo1a5Y33ngj/fr1O6++KisrM23atAwdOjRVVVX5+eef06NHj5w8eTLV1dVJkhEjRuSVV15JcXFx/bp9+/blgQceyNatW1NUVJTOnTunefPm+fbbb3PixImUlpZmyZIlueqqq+rXjB8/Pps3b06/fv3y9ddfp1evXtm3b1/Ky8vz8ssvJ0lmzpyZ5cuXJ0k6dOiQtm3bprq6OocOHUpJSUkqKirSoUOH89ozAAAA586XuQAAAFzSvvzyy8yaNSsnT57MY489ls8//zxvv/12Pv3007z00ktp1qxZlixZkoqKijPWVVdXZ8uWLZk/f34+++yzrFq1KuvWrUvPnj1z9OjRLFy48IL1+NFHH+Xw4cN5880388EHH+TDDz/M0qVL06pVq6xZsyYrVqw4o/7pp5/O1q1b079//6xZsyZr167Nu+++m3Xr1mXgwIHZvn17pk+f3ui7vv7667z22mt57733sn79+vq6ysrKLF++PC1atMirr76a9evXZ9WqVfnoo49SVlaW2traTJs27YLtGQAAgD8nzAUAAOCStnDhwtTV1WXMmDGZPHly/ReuTZo0yejRozN16tQkyYIFC3LixIkz1j700EO544476v+XlJRk0qRJSU6FohfSCy+8kEGDBtX/Hzx4cJ555pkkyaJFi3L6YK1t27bl448/Tps2bbJw4cJ06dKlfk3Hjh2zYMGCtGzZMuvWrcvOnTsbvKd///4ZPnx4kqSoqCht2rRJkrz++utJkieeeCK33XZbfX3btm0zd+7cFBcXZ9OmTdmzZ88F3TcAAABnJ8wFAADgknXgwIF89dVXSZJx48Y1WjNmzJg0a9YstbW12b59+xlzN998c4P6bt26JUn++OOPC9Znp06dMmzYsAbjo0aNyuWXX57a2tpUVVUlSdatW5ckufHGG9O2bdsGa66++uqUl5cnSTZs2NBgvrGjoWtqalJTU5Pi4uKMHj26wXxJSUlWrVqVL774Iu3bt/9rmwMAAOD/rPjPSwAAAOD/px9//DHHjx9P06ZNc/311zdac/nll6d79+7ZuXNnampqzghIGwsumzdvniQNvuI9H3369Gl0vFmzZunatWt27tyZXbt2pbS0NN99912S5KuvvsrYsWMbXbd79+4kqb9399+1a9euwdgPP/yQ5FSo3KJFi0af2atXrz/fCAAAABeUMBcAAIBL1oEDB5KcCmyLis5+ONXpAPN0/WlNmzb9+5r7N1deeeVZ51q2bJnkX18Cn/6tra1NbW3tf3zu/v37G4xddtllDcb27duXJGcNcgEAALg4hLkAAABcsk4HoYcOHcrJkyfPGuieDkgvVph56NChs86d7u2qq65KciqYTpKnnnoq999//wV5/+ln/qc+AAAA+Oe5MxcAAIBLVufOnVNcXJxjx47lm2++abTm4MGD9ccRd+3a9Z9sr97po5P/tyNHjqSmpiZJ0qNHjyT/6vFsa5KkqqoqO3bsOOd7fa+77rokp45nPnz4cKM1M2bMyKRJk7J58+ZzeiYAAADnT5gLAADAJatly5YZOHBgkmTFihWN1qxcuTLHjh1LmzZtUlpa+k+2V2/Hjh3ZuXNng/F33303R44cSffu3dO9e/ckyS233JIkWbt2bfbu3dtgzf79+zNhwoSMHj06q1evPqf39+zZMx07dszx48fz/vvvN5j/9ddf895772X9+vW54oor/sLOAAAAOB/CXAAAAC5pU6ZMSVFRUd56660sWrQox48fT5LU1dXlnXfeybx585IkDz/88D92R25jHn300fovhJNkw4YNefHFF5MkjzzySP14WVlZBg0alN9//z2TJ0/Orl276uf27NmTKVOm5Lfffku7du0yatSoc3p3kyZNMmnSpCTJnDlzsnHjxvq5vXv35vHHH8/BgwdTVlaWG2644bz2CQAAwLlzZy4AAACXtLKyskyfPj2zZ8/OvHnzsmTJknTp0iU//fRTfvnllyTJfffdl3vuueei9diuXbvs3bs3d9xxR3r16pXDhw/XH698//335/bbbz+jft68eZk4cWK2bt2aESNGpGfPnikqKsr333+fY8eOpVWrVlm8eHGaN29+zj2MGzcuVVVVqaioyMSJE3PttdemVatWqa6uzpEjR9KpU6fMmTPnQm4bAACAPyHMBQAA4JJ37733pm/fvlm6dGk2b96cHTt2pG3bthkxYkTGjh2bwYMHX9T+SkpKMn/+/MydOzebNm1KkyZNMnjw4Nx333259dZbG9S3b98+FRUVWbZsWVavXl0f4paUlGTIkCGZNGlSrr322r/cx/PPP5+bbropK1asyPbt27Nnz5507Ngxw4cPz+TJk9O6desLsV0AAADOUZO6urq6i90EAAAA/DeqrKzMtGnTUlpamsrKyovdDgAAAAXGnbkAAAAAAAAABUiYCwAAAAAAAFCA3JkLAAAA52HWrFmpqqr6y+ueffbZv6EbAAAALiXCXAAAADgP33zzTbZs2fKX1+3fv/9v6AYAAIBLSZO6urq6i90EAAAAAAAAAGdyZy4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAARLmAgAAAAAAABQgYS4AAAAAAABAAfofezxZslMDvlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 953
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.scatterplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')\n",
    "sns.lineplot(data=df, x='on_perc', y='val_acc_max', hue='model')\n",
    "plt.errorbar(x=[0.02, 0.04], y=[0.75, 0.85], yerr=[0.1, 0.01], color='k', marker='.', lw=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x146c53a20>"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 944
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 16, 8\n",
    "filter = df['model'] != 'Static'\n",
    "plt.errorbar(\n",
    "    x=[0.02, 0.04],\n",
    "    y=[85, 95],\n",
    "    yerr=[1, 1],\n",
    "    color='k',\n",
    "    marker='*',\n",
    "    lw=0,\n",
    "    elinewidth=2,\n",
    "    capsize=2,\n",
    "    markersize=10,\n",
    ")\n",
    "sns.lineplot(data=df[filter], x='on_perc', y='val_acc_max_epoch', hue='model2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 938
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(\n",
    "    x=[0.02, 0.04],\n",
    "    y=[0.85, 0.95],\n",
    "    yerr=[0.01, 0.01],\n",
    "    color='k',\n",
    "    marker='.',\n",
    "    lw=0,\n",
    "    elinewidth=1,\n",
    "    capsize=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13a823c18>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 953
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df, x='on_perc', y='val_acc_last', hue='model2')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
